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October 2, 2025The Astrophysical Journal4 citationsOpen Access

TransFit: An Efficient Framework for Transient Light-curve Fitting with Time-dependent Radiative Diffusion

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LLLiang-Duan LiuYZY.-H. ZhangYYYun-Wei Yu

Key Points

  • TransFit accurately captures the influence of heating sources on light curve morphology, improving model reliability.
  • The framework integrates time-dependent radiative diffusion, addressing limitations of previous models and increasing computational efficiency.
  • By modeling shock-cooling and radioactive heating transitions, TransFit provides deeper insights into transient behavior over time.
  • This tool aims to facilitate data analysis for upcoming astronomical transient surveys, enhancing our understanding of supernovae.

Abstract

Abstract Modeling the light curves (LCs) of luminous astronomical transients, such as supernovae, is crucial for understanding their progenitor physics, particularly with the exponential growth of survey data. However, existing methods face limitations: efficient semianalytical models (e.g., Arnett-like) employ significant physical simplifications (like time-invariant temperature profiles and simplified heating distributions), often compromising accuracy, especially for early-time LCs. Conversely, detailed numerical radiative transfer simulations, while accurate, are computationally prohibitive for large data sets. This paper introduces TransFit , a novel framework that numerically solves a generalized energy conservation equation, explicitly incorporating time-dependent radiative diffusion, continuous radioactive or central engine heating, and ejecta expansion dynamics. The model accurately captures the influence of key ejecta properties and diverse heating source characteristics on LC morphology, including peak luminosity, rise time, and overall shape. Furthermore, TransFit provides self-consistent modeling of the transition from shock-cooling to 56 Ni-powered LCs. By combining physical realism with computational speed, TransFit provides a powerful tool for efficiently inverting LCs and extracting detailed physical insights from the vast data sets of current and future transient surveys.

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Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/68de6f3f83cbc991d0a22a64https://doi.org/10.3847/1538-4357/adfed6
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